Binary Neural Networks (BNNs), neural networks with weights and activations constrained to -1(0) and +1, are an alternative to deep neural networks which offer faster training, lower memory consumption and lightweight models, ideal for use in resource constrained devices while being able to utilize the architecture of their deep neural network counterpart. However, the input binarization step used in BNNs causes a severe accuracy loss. In this paper, we introduce a novel hybrid neural network architecture, Hybrid Binary Neural Network (HyBNN), consisting of a task-independent, general, full-precision variational autoencoder with a binary latent space and a task specific binary neural network that is able to greatly limit the accuracy loss due to input binarization by using the full precision variational autoencoder as a feature extractor. We use it to combine the state-of-the-art accuracy of deep neural networks with the much faster training time, quicker test-time inference and power efficiency of binary neural networks. We show that our proposed system is able to very significantly outperform a vanilla binary neural network with input binarization. We also introduce FedHyBNN, a highly communication efficient federated counterpart to HyBNN and demonstrate that it is able to reach the same accuracy as its non-federated equivalent. We make our source code, experimental parameters and models available at: https://anonymous.4open.science/r/HyBNN.

本文介绍了一种新颖的混合神经网络结构，Hybrid Binary Neural Network（HyBNN），其由一个任务无关的、通用的、全精度变分自动编码器和一个任务特定的二进制神经网络组成，它能够通过使用全精度变分自动编码器作为特征提取器大大限制输入二元化引起的准确性损失，结合深度神经网络的最先进的准确性以及二进制神经网络的更快的训练时间、更快的测试时间推理和更高的功率效率。我们展示了我们提出的系统能够明显优于常规的二进制神经网络。此外,我们还介绍了FedHyBNN，这是HyBNN的高度通信效率的联邦学习版本，并证明它能够达到与其非联邦版本相同的准确性。

HyBNN 和 FedHyBNN：（联邦）混合二进制神经网络